Electrical impedance tomography based on BP neural network and improved PSO

A new method for static electrical impedance tomography was proposed in this paper. The new algorithm was based on the weight adjustments of error back propagation of BP neural network whose weights and thresholds were modified by improved particle swarm optimization. This method can not only well a...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 1059 - 1064
Main Authors Peng Wang, Li-Li Xie, Yi-Cai Sun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
BP neural network
Convergence
Cybernetics
Electrical impedance tomography
High-resolution imaging
Image reconstruction
Impedance
Improved particle swarm optimization
Iterative algorithms
Machine learning
Neural networks
Particle swarm optimization
Threshold adjustment
Tomography
Weight adjustment
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Summary:A new method for static electrical impedance tomography was proposed in this paper. The new algorithm was based on the weight adjustments of error back propagation of BP neural network whose weights and thresholds were modified by improved particle swarm optimization. This method can not only well adapt to non-linear and ill-posed characteristics of electrical impedance tomography, but also overcome the limitations both the slow convergence and the local extreme values by basic BP algorithm. The improved particle swarm optimization has less iteration and higher accuracy then the standard particle swarm optimization. Experimental results show that the method is easy, fast and can effectively improve the image resolution.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212387